topic_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/serkan/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "import re\n",
    "import warnings\n",
    "from collections import Counter\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation as LDA\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "nltk.download('punkt')\n",
    "warnings.simplefilter(\"ignore\", DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['acaba', 'ama', 'aslında', 'az', 'bazı', 'belki', 'biri', 'birkaç', 'birşey', 'biz', 'bu', 'çok', 'çünkü', 'da', 'daha', 'de', 'defa', 'diye', 'eğer', 'en', 'gibi', 'hem', 'hep', 'hepsi', 'her', 'hiç', 'için', 'ile', 'ise', 'kez', 'ki', 'kim', 'mı', 'mu', 'mü', 'nasıl', 'ne', 'neden', 'nerde', 'nerede', 'nereye', 'niçin', 'niye', 'o', 'sanki', 'şey', 'siz', 'şu', 'tüm', 've', 'veya', 'ya', 'yani']\n"
     ]
    }
   ],
   "source": [
    "WPT = nltk.WordPunctTokenizer()\n",
    "stop_word_list = nltk.corpus.stopwords.words('turkish')\n",
    "print(stop_word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('topic_modeling.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>Document</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>astronomi</td>\n",
       "      <td>NGC 5713 Başak takımyıldızı bölgesinde bulunan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>astronomi</td>\n",
       "      <td>Birçok katalogda sarmal gökada olarak sınıflan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>hükümet</td>\n",
       "      <td>Corina Casanova , İsviçre Federal Şansölyesidir .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>yasa</td>\n",
       "      <td>Casanova , İsviçre Federal Yüksek Mahkemesi es...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>hükümet</td>\n",
       "      <td>Corina Casanova bir federal parlementerdir .</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Class                                           Document\n",
       "0  astronomi  NGC 5713 Başak takımyıldızı bölgesinde bulunan...\n",
       "1  astronomi  Birçok katalogda sarmal gökada olarak sınıflan...\n",
       "2    hükümet  Corina Casanova , İsviçre Federal Şansölyesidir .\n",
       "3       yasa  Casanova , İsviçre Federal Yüksek Mahkemesi es...\n",
       "4    hükümet       Corina Casanova bir federal parlementerdir ."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(647809, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 647809 entries, 0 to 647808\n",
      "Data columns (total 2 columns):\n",
      "Class       647809 non-null object\n",
      "Document    647809 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 9.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['astronomi', 'hükümet', 'yasa', 'bölge', 'televizyon', 'bilgisayar', 'işletme', 'film', 'tıp', 'askeri', 'zaman', 'organizasyon', 'spor', 'futbol', 'inşaat', 'coğrafya', 'müzik', 'oyunlar', 'insanlar', 'kraliyet hanedanı', 'eğitim', 'ödül', 'biyoloji', 'internet', 'simgeler', 'kitaplar', 'ortak_medya', 'görsel_sanat', 'travel', 'kurgusal_evren', 'havacılık', 'ulaşım', 'kimya', 'dil', 'finans', 'otomotiv', 'opera', 'çizgiromanlar', 'basketbol', 'yemek', 'interests', 'tiyatro', 'din', 'ölçü_birimi', 'moda', 'meteoroloji', 'mühendislik', 'exhibitions', 'fizik'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#etiketlerin elde edilmesi\n",
    "class_ = Counter(data['Class']).keys()\n",
    "class_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([4997, 19921, 3083, 89926, 20612, 7366, 8258, 42623, 12107, 21378, 11146, 14847, 32363, 8560, 5023, 7340, 53235, 5685, 129943, 7683, 8423, 14214, 18917, 2357, 5160, 21726, 6155, 3984, 3552, 7883, 3982, 4156, 4696, 5352, 942, 2448, 4125, 1782, 3721, 4222, 1479, 2101, 6734, 1228, 460, 212, 1078, 129, 495])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#etiketlerin içerisindeki veri sayıları\n",
    "sum_ = Counter(data['Class']).values()\n",
    "sum_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>Toplam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>astronomi</td>\n",
       "      <td>4997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>hükümet</td>\n",
       "      <td>19921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>yasa</td>\n",
       "      <td>3083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>bölge</td>\n",
       "      <td>89926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>televizyon</td>\n",
       "      <td>20612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Class  Toplam\n",
       "0   astronomi    4997\n",
       "1     hükümet   19921\n",
       "2        yasa    3083\n",
       "3       bölge   89926\n",
       "4  televizyon   20612"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(zip(class_,sum_), columns = ['Class', 'Toplam'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#etiketlerin görselleştirilmesi\n",
    "df.plot(x = 'Class' , y = 'Toplam',kind = 'bar', legend = False, grid = True, figsize = (15,5))\n",
    "plt.title('Kategori Sayılarının Görselleştirilmesi', fontsize = 20)\n",
    "plt.xlabel('Kategoriler', fontsize = 15)\n",
    "plt.ylabel('Toplam', fontsize = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#etiketlerin görselleştirilmesi - pasta grafiği\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "ax.pie(df.Toplam, labels =df.Class, autopct = '%1.1f%%',  startangle = 90 )\n",
    "ax.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = data['Document']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#verinin temizlenmesi\n",
    "docs = docs.map(lambda x: re.sub('[,\\.!?();:$%&#\"]', '', x))\n",
    "docs = docs.map(lambda x: x.lower())\n",
    "docs = docs.map(lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#verideki stopword kelimelerin temizlenmesi\n",
    "def token(values):\n",
    "    filtered_words = [word for word in values.split() if word not in stop_word_list]\n",
    "    not_stopword_doc = \" \".join(filtered_words)\n",
    "    return not_stopword_doc\n",
    "\n",
    "docs = docs.map(lambda x: token(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Class                                           Document\n",
      "0    astronomi  ngc 5713 başak takımyıldızı bölgesinde bulunan...\n",
      "1    astronomi  birçok katalogda sarmal gökada olarak sınıflan...\n",
      "2      hükümet     corina casanova i̇sviçre federal şansölyesidir\n",
      "3         yasa  casanova i̇sviçre federal yüksek mahkemesi esk...\n",
      "4      hükümet         corina casanova bir federal parlementerdir\n",
      "5        bölge  casanova hristiyan demokrat halk partisi üyesi...\n",
      "6      hükümet  i̇sviçre dışişleri bakanlığı i̇sviçre federal ...\n",
      "7      hükümet  i̇sviçre'nin dış ilişkilerini sürdürmekle göre...\n",
      "8        bölge  gilgit baltistan pakistan kuzey bölgeler urduc...\n",
      "9        bölge  72496 km alan kaplamakta oldukça dağlık bir ar...\n",
      "10       bölge                       bölgesel başkenti gilgit'dir\n",
      "11       bölge  urduca peştuca wakhi shina balti khuar buruşas...\n",
      "12       bölge  denton amerika birleşik devletleri'nde teksas ...\n",
      "13       bölge  coğrafî olarak oklahoma teksas sınırının 61 ki...\n",
      "14       bölge  şehir kuzey teksas'ın büyük üniversitesi olan ...\n",
      "15  televizyon   howard stark marvel'ın kurgusal bir karakteridir\n",
      "16  bilgisayar  mac os x snow leopard sürüm 106 türkçe kar leo...\n",
      "17  bilgisayar  i̇lk genel gösteri dünya çapında pazarlamadan ...\n",
      "18  bilgisayar  mac os x'in sürümü selefi mac os x v105 leopar...\n",
      "19  bilgisayar  aynı zamanda ilk mac os sürümü system 712'den ...\n"
     ]
    }
   ],
   "source": [
    "data['Document'] = docs\n",
    "data['Document'] = data['Document'].map(lambda x: x.strip())\n",
    "print(data.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sınıfların ve verilerin ayrılması\n",
    "dataDoc = data['Document'].values.tolist()\n",
    "dataClass = data['Class'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'spor'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataClass[301]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"aek takımından serbest bırakıldıktan sonra abd'nin major league futbol takımlarından seattle sounders fc deneme antrenmanlarına çıktı\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataDoc[301]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test ve train olarak verinin ayrılması\n",
    "x_train, x_test, y_train, y_test = train_test_split(dataDoc, dataClass, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#veride en çok geçen 20 kelimenin hesaplanması\n",
    "words_counts = Counter([word for line in x_train for word in line.split(' ')])\n",
    "\n",
    "# Sorting \n",
    "most_common_words = sorted(words_counts.items(), key=lambda x: x[1], reverse=True)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bir', 121182),\n",
       " ('olarak', 48115),\n",
       " ('olan', 39859),\n",
       " ('yılında', 30484),\n",
       " ('tarafından', 24377),\n",
       " ('sonra', 23792),\n",
       " ('ilk', 23313),\n",
       " ('büyük', 16955),\n",
       " ('yer', 15010),\n",
       " ('arasında', 12783),\n",
       " ('oldu', 12070),\n",
       " ('kadar', 11815),\n",
       " ('the', 11342),\n",
       " ('2', 10580),\n",
       " ('iki', 10370),\n",
       " ('bulunan', 9993),\n",
       " ('aynı', 9876),\n",
       " ('1', 9866),\n",
       " ('dünya', 9489),\n",
       " ('adlı', 9265)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_common_words[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tfidf işlemi\n",
    "tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,2), max_df=0.9, min_df = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_tfidf = tfidf_vectorizer.fit_transform(x_train)\n",
    "x_test_tfidf = tfidf_vectorizer.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OneVsRest LogisticRegression modeli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/serkan/.local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None,\n",
       "                                                 dual=False, fit_intercept=True,\n",
       "                                                 intercept_scaling=1,\n",
       "                                                 l1_ratio=None, max_iter=100,\n",
       "                                                 multi_class='warn',\n",
       "                                                 n_jobs=None, penalty='l2',\n",
       "                                                 random_state=None,\n",
       "                                                 solver='warn', tol=0.0001,\n",
       "                                                 verbose=0, warm_start=False),\n",
       "                    n_jobs=None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = OneVsRestClassifier(LogisticRegression(penalty = 'l2', C=1.0))\n",
    "model.fit(x_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " 'insanlar',\n",
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       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
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       " 'insanlar',\n",
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       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " 'insanlar',\n",
       " ...]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test_tfidf).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.97538537, -7.07945617, -4.70773674, ..., -6.88647999,\n",
       "        -5.96437229, -7.15402154],\n",
       "       [-3.76378967, -5.71507997, -6.09378337, ..., -6.08662361,\n",
       "        -5.11872298, -6.7148944 ],\n",
       "       [-5.64901265, -5.83823835, -6.37195696, ..., -6.09262843,\n",
       "        -4.74208529, -6.37590263],\n",
       "       ...,\n",
       "       [-4.73053829, -6.52901107, -6.20988444, ..., -6.76878406,\n",
       "        -5.4033885 , -6.99988466],\n",
       "       [-4.57542839, -5.69713915, -5.94095319, ..., -6.52868335,\n",
       "        -4.70341505, -6.60203887],\n",
       "       [-5.06545973, -6.16222853, -6.08072178, ..., -6.50619661,\n",
       "        -5.87224087, -6.54742038]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.decision_function(x_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accracy=0.6042203732575909\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print (\"Logistic Regression Accracy={}\".format(accuracy_score(y_test, model.predict(x_test_tfidf))))\n",
    "logisticpred = accuracy_score(y_test, model.predict(x_test_tfidf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OvR Stochastic Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/serkan/.local/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:561: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=SGDClassifier(alpha=0.0001, average=False,\n",
       "                                            class_weight=None,\n",
       "                                            early_stopping=False, epsilon=0.1,\n",
       "                                            eta0=0.0, fit_intercept=True,\n",
       "                                            l1_ratio=0.15,\n",
       "                                            learning_rate='optimal',\n",
       "                                            loss='hinge', max_iter=5,\n",
       "                                            n_iter_no_change=5, n_jobs=None,\n",
       "                                            penalty='elasticnet', power_t=0.5,\n",
       "                                            random_state=None, shuffle=True,\n",
       "                                            tol=0.001, validation_fraction=0.1,\n",
       "                                            verbose=0, warm_start=False),\n",
       "                    n_jobs=None)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = OneVsRestClassifier(SGDClassifier(loss = 'hinge', penalty = 'elasticnet', max_iter = 5))\n",
    "model2.fit(x_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['insanlar', 'bölge', 'insanlar', ..., 'insanlar', 'mühendislik',\n",
       "       'spor'], dtype='<U17')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict(x_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGD Accuracy=0.45770107490364975\n"
     ]
    }
   ],
   "source": [
    "print (\"SGD Accuracy={}\".format(accuracy_score(y_test, model2.predict(x_test_tfidf))))\n",
    "sgdpred = accuracy_score(y_test, model2.predict(x_test_tfidf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OvR LineerSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('clf',\n",
       "                 OneVsRestClassifier(estimator=LinearSVC(C=1.0,\n",
       "                                                         class_weight=None,\n",
       "                                                         dual=True,\n",
       "                                                         fit_intercept=True,\n",
       "                                                         intercept_scaling=1,\n",
       "                                                         loss='squared_hinge',\n",
       "                                                         max_iter=1000,\n",
       "                                                         multi_class='ovr',\n",
       "                                                         penalty='l2',\n",
       "                                                         random_state=None,\n",
       "                                                         tol=0.0001,\n",
       "                                                         verbose=0),\n",
       "                                     n_jobs=1))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC_pipeline = Pipeline([\n",
    "                ('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),\n",
    "            ])\n",
    "SVC_pipeline.fit(x_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LineerSVC accuracy is 0.645472180629094\n"
     ]
    }
   ],
   "source": [
    "prediction = SVC_pipeline.predict(x_test_tfidf)\n",
    "print('LineerSVC accuracy is {}'.format(accuracy_score(y_test, prediction)))\n",
    "lineersvcpred = accuracy_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracys = [logisticpred,lineersvcpred,sgdpred]\n",
    "plt.plot([\"Log\", \"LineerSVC\", \"SGD\"],accuracys , marker = \"x\" , markersize = 9 , color = \"red\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LDA modeli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#denetimsiz olarak modeli tahmin etmek için countvectorizer ile train verisini sayısal hale getiriyoruz.\n",
    "tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=10000)\n",
    "tf = tf_vectorizer.fit_transform(dataDoc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_feature_names = tf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modelin oluşturulması\n",
    "lda = LDA(n_components=30, max_iter=5, learning_method='online', learning_offset=50.,random_state=0).fit(tf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:\n",
      "bir küçük şekilde on iki talyan türü spor ilgili japon olur dizi kız amacıyla yerel idi filmidir oyuncusudur familyasından adam adında yahudi dizisi komedi verdiği kullanılır kitap çocuğu tamamen şeklinde\n",
      "Topic 1:\n",
      "nın dünya nda kupası savaşı ii yapılan sırasında kazandı gün pek şampiyonası doğru 1992 yaz nı fifa olimpiyatları yunanistan kinci karşı azerbaycan şampiyon madalya kulüp brezilya oyunları doğusunda altın stadyumu\n",
      "Topic 2:\n",
      "birlikte ye alman ocak uzun kısa 12 de temsil geniş arkadaşı parti tan lı asıllı para aktör halen eşi macaristan doğan yu von kısmı kodu kasım yalnızca bulundu destek yıllar\n",
      "Topic 3:\n",
      "da de oldu ya yeni ilk almanya başladı nın olarak sonunda ulusal grup kişi kurulan talya başkenti sahibi yaşında londra amerikalı girdi başlayan hollanda şehrinde 1986 düzenlenen polonya 1984 2014\n",
      "Topic 4:\n",
      "tarafından ikinci yılında şarkı üzere 2004 albümü albüm olmak çıkan sahiptir isimli rock sıra 1996 albümün stüdyo yanı ev ndan sanatçı şarkısı iç güzel yayınlandı yarışması eurovision başta jackson 1991\n",
      "Topic 5:\n",
      "iki iyi devam roma rağmen cumhuriyeti rusya savaş geri yaklaşık erkek kadın ortaya profesyonel kalan mparatorluğu nin insanlar buna başlar akdeniz etmektedir olmuş person_name bile bilgi var adet hala yaş\n",
      "Topic 6:\n",
      "tarihinde yıl etti yi önce 10 ekim na ağustos nisan osmanlı mart ran olarak 25 30 tam mö rus grubun eyaleti 28 insan yanında bin 22 2009 kendisine devleti yardımcı\n",
      "Topic 7:\n",
      "yer arasında olarak aldı mayıs kabul aralık yılları görev haziran 2009 alır ın sırada almaktadır 100 yol 26 11 böylece edilir güneyinde 1970 bilinir video 27 yapmış tarihleri listesinde kudüs\n",
      "Topic 8:\n",
      "olan bir ın kuzey şehir sahip nin kuzeyinde yaşayan nehri inşa içinde yıldız özerk yunan ilçe dönem merkezi bunu hindistan bölgesinin batısında sınırları ailesi dr köy kenti bölgesi olma tanrı\n",
      "Topic 9:\n",
      "abd alan bir adı üç li olarak ana eder sinema genellikle de yazarı oldukça söz şarkıcı tiyatro verilen şirketi modern taşıyan türkçe sistemi benzer güneş babasının sanatçısı verilmiştir bölgede 1966\n",
      "Topic 10:\n",
      "bir avrupa önemli diğer türk da güney doğu içinde batı çeşitli tarihi anadolu yapmıştır birliği afrika deniz bulunur dışında çin başarılı mısır ordu 1980 asya tür veren antik sonrasında suriye\n",
      "Topic 11:\n",
      "yılında the film ancak gelen üzerine of televizyon almıştır filmi 2002 2003 rol ismi tekrar filmde aldığı and 2007 yönetmen oynadığı 1997 filminde sahne kuruldu adıyla 1982 filmin grup meydana\n",
      "Topic 12:\n",
      "büyük olarak son birçok zaman tarafından sadece fakat yine özel dört kullanılan gelir altında yıllarda askeri hava nedeniyle anlamına kendini kara sayıda ortaya olsa adası 1972 uygun 1974 sonradan gösteren\n",
      "Topic 13:\n",
      "2010 2011 2012 da tarafından fransız de yapımı yeniden el 2013 john bütün edildi 1990 olması itibaren toplam yılının san hiçbir 1960 çıkmıştır temmuz ses 1976 to gece mart 1965\n",
      "Topic 14:\n",
      "km de ligi takımı milli sezonunda deki 16 lig 18 gol takım forma sezon maçta 2001 transfer 13 verdi 17 1999 24 oyun mücadele sezonu le başına fc oynadı maçında\n",
      "Topic 15:\n",
      "olmuştur üniversitesi stanbul de eylül 2007 tek uluslararası yapan yerine beri eğitim ankara teknik srail olarak üst yılından 29 avustralya sosyal zmir yapılmıştır yapmaktadır ekonomik kentinde tarih lisesi değildir gitar\n",
      "Topic 16:\n",
      "bağlı nın futbol daki ne yı spanya bulunan eyaletinde millî çıktı iline ülkenin bölgesi bilim katıldı schleswig holstein nin ortak ilçesine takımı sona denizi moskova rlanda bölgesinden yüzölçüme gölü köydür\n",
      "Topic 17:\n",
      "eski müzik yüksek ngiliz ödülü grubu kasım ünlü de ayında sonraki başlamıştır açık haline olarak metal altın 1993 yılın çocuk kazanan pop kazanmıştır general george okulu çalıştı sayesinde bey hayatını\n",
      "Topic 18:\n",
      "türkiye ayrıca 2008 nin 20 yaptığı temmuz 19 olarak bunun edilen ardından 21 günü piyasaya edilmiştir ilan takımının alanı 2006 kıbrıs yönetmenliğini records city kültür girmiştir al yüzyılın buz denir\n",
      "Topic 19:\n",
      "un dan oyuncu olduğunu ın süre onun genç filmin onu ni kardeşi kralı alt 14 ele gerçek kendisi david james kral güçlü fenerbahçe kendisini bağımsız william baş britanya üzerinden süren\n",
      "Topic 20:\n",
      "nun genel lk biridir 2006 milyon 1998 bulunduğu ülke single yıllarında dünyanın kanada kurulmuş avusturya roman bugün olmasına belediyesi şarkının berlin başkan nin dur kurulu yayın yaşadığı çapında karşın tanınan\n",
      "Topic 21:\n",
      "bir eden la adını geldi oyuncusu dolayı günümüzde dünyaya karar süper olmayan kent önceki arası metre artık 1985 çalışan takımın seçildi 1981 konu galatasaray ifade doğumlu hareket 40 içeren beşiktaş\n",
      "Topic 22:\n",
      "nde bir orta fazla farklı 23 sanat aday geçti bölümü michael etmek şehirdir ödülleri festivali sonucunda ederek normal ödül yönetmeni bilgisayar kullanarak akademi şehridir dış yok kemal demir windows nin\n",
      "Topic 23:\n",
      "ilk aynı olan den olarak zamanda yılı bölge merkezi dir nin nüfusu resmi yönetim halk ngilizce partisi altı kaldı ten üyesi ilçesi dil 2006 şehrin yeri yüzyılda yana paul dili\n",
      "Topic 24:\n",
      "bulunan 2005 futbolcu da 15 yazar ait su beraber ün hemen basketbol kulübü nba beyaz sveç ali yedi mustafa vardı 1977 itibariyle 1969 kendine muhammed zengin is arapça all star\n",
      "Topic 25:\n",
      "sonra kadar bir göre kendi yaptı bulunmaktadır boyunca 2000 de burada dan ordusu birinci doğdu beş yılına ay nüfus 1987 hafta döndü bundan serbest aile yüzyıl takımlarından ölümünden sene tur\n",
      "Topic 26:\n",
      "bir adlı olduğu üzerinde olup şubat başka ilinin oluşan dır sonucu hakkında 1995 ili içerisinde yerleşim internet 1988 dahil yapılan sürekli yaygın uzay il yoğun ilçesidir jack sayısı çizgi karakteri\n",
      "Topic 27:\n",
      "in ın te vardır karşı nin üçüncü yakın babası oğlu paşa paris 1994 kızı annesi yazdığı sultan temel dördüncü siyasi doğal ardından iii 1983 mehmet kötü anne ahmet ressam lideri\n",
      "Topic 28:\n",
      "fransa amerika devlet birleşik new özellikle başkanı olarak döneminde york geçen ona kurulmuştur nden merkez yıllık devletleri krallık japonya belediye değil birisidir robert oyunu adına edilmiş adamı bölümünde sovyet krallığı\n",
      "Topic 29:\n",
      "bir ta olarak ngiltere amerikan bilinen elde etmiştir adıyla bulunan dönemde arasındaki şehri nin bölgesinde yayınlanan çoğu los iş sviçre yardım angeles yönünde oynamaktadır an yaklaşık ngc bizans buffy savunma\n"
     ]
    }
   ],
   "source": [
    "def display_topics(model, feature_names, no_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic %d:\" % (topic_idx))\n",
    "        print(\" \".join([feature_names[i]\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]]))\n",
    "display_topics(lda,tf_feature_names,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#!pip3 install pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis.sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/serkan/.local/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    }
   ],
   "source": [
    "#tahmin edilen konuların görselleştirilmesi\n",
    "pyLDAvis.enable_notebook()\n",
    "panel = pyLDAvis.sklearn.prepare(lda,tf, tf_vectorizer, mds = 'tsne' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el86581406841423500325163077803\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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\"ana\", \"eder\", \"sinema\", \"genellikle\", \"yazar\\u0131\", \"olduk\\u00e7a\", \"s\\u00f6z\", \"\\u015firketi\", \"modern\", \"ta\\u015f\\u0131yan\", \"t\\u00fcrk\\u00e7e\", \"sistemi\", \"benzer\", \"g\\u00fcne\\u015f\", \"babas\\u0131n\\u0131n\", \"sanat\\u00e7\\u0131s\\u0131\", \"verilmi\\u015ftir\", \"b\\u00f6lgede\", \"1966\", \"e\\u011fitimi\", \"1964\", \"yazd\\u0131\", \"richard\", \"bunlar\\u0131n\", \"henry\", \"tiyatro\", \"\\u015fark\\u0131c\\u0131\", \"verilen\", \"bir\", \"olarak\", \"de\", \"ba\\u011fl\\u0131\", \"futbol\", \"daki\", \"ne\", \"y\\u0131\", \"spanya\", \"eyaletinde\", \"mill\\u00ee\", \"\\u00e7\\u0131kt\\u0131\", \"iline\", \"\\u00fclkenin\", \"bilim\", \"kat\\u0131ld\\u0131\", \"schleswig\", \"holstein\", \"ortak\", \"il\\u00e7esine\", \"sona\", \"denizi\", \"moskova\", \"rlanda\", \"b\\u00f6lgesinden\", \"y\\u00fcz\\u00f6l\\u00e7\\u00fcme\", \"g\\u00f6l\\u00fc\", \"k\\u00f6yd\\u00fcr\", \"kalm\\u0131\\u015ft\\u0131r\", \"meksika\", \"eyaletine\", \"thomas\", \"kurgu\", \"b\\u00f6lgesi\", \"n\\u0131n\", \"bulunan\", \"tak\\u0131m\\u0131\", \"nin\", \"iyi\", \"devam\", \"roma\", \"ra\\u011fmen\", \"cumhuriyeti\", \"rusya\", \"sava\\u015f\", \"erkek\", \"kad\\u0131n\", \"profesyonel\", \"kalan\", \"mparatorlu\\u011fu\", \"insanlar\", \"buna\", \"ba\\u015flar\", \"akdeniz\", \"etmektedir\", \"olmu\\u015f\", \"person_name\", \"bile\", \"bilgi\", \"var\", \"adet\", \"hala\", \"ya\\u015f\", \"ki\", \"b\\u00f6l\\u00fcm\", \"ukrayna\", \"oynam\\u0131\\u015ft\\u0131r\", \"i\\u015fgal\", \"iki\", \"geri\", \"yakla\\u015f\\u0131k\", \"ortaya\", \"nin\", \"adl\\u0131\", \"oldu\\u011fu\", \"\\u00fczerinde\", \"olup\", \"\\u015fubat\", \"ba\\u015fka\", \"ilinin\", \"olu\\u015fan\", \"d\\u0131r\", \"sonucu\", \"hakk\\u0131nda\", \"1995\", \"ili\", \"i\\u00e7erisinde\", \"yerle\\u015fim\", \"internet\", \"1988\", \"s\\u00fcrekli\", \"yayg\\u0131n\", \"uzay\", \"il\", \"yo\\u011fun\", \"il\\u00e7esidir\", \"jack\", \"say\\u0131s\\u0131\", \"\\u00e7izgi\", \"karakteri\", \"ticari\", \"belli\", \"kraliyet\", \"bir\", \"dahil\", \"yap\\u0131lan\", \"2010\", \"2011\", \"2012\", \"frans\\u0131z\", \"yap\\u0131m\\u0131\", \"yeniden\", \"el\", \"2013\", \"john\", \"b\\u00fct\\u00fcn\", \"edildi\", \"1990\", \"olmas\\u0131\", \"itibaren\", \"toplam\", \"y\\u0131l\\u0131n\\u0131n\", \"san\", \"hi\\u00e7bir\", \"1960\", \"\\u00e7\\u0131km\\u0131\\u015ft\\u0131r\", \"ses\", \"1976\", \"to\", \"gece\", \"1965\", \"g\\u00f6r\\u00fcl\\u00fcr\", \"d\\u00f6nemin\", \"sat\\u0131\\u015fa\", \"merkezidir\", \"1930\", \"taraf\\u0131ndan\", \"da\", \"de\", \"temmuz\", \"mart\", \"ta\", \"ngiltere\", \"amerikan\", \"bilinen\", \"elde\", \"etmi\\u015ftir\", \"d\\u00f6nemde\", \"aras\\u0131ndaki\", \"\\u015fehri\", \"b\\u00f6lgesinde\", \"yay\\u0131nlanan\", \"\\u00e7o\\u011fu\", \"los\", \"i\\u015f\", \"svi\\u00e7re\", \"yard\\u0131m\", \"angeles\", \"y\\u00f6n\\u00fcnde\", \"oynamaktad\\u0131r\", \"an\", \"ngc\", \"bizans\", \"buffy\", \"savunma\", \"y\\u00f6netimi\", \"i\\u00e7ine\", \"g\\u00f6\\u00e7\", \"\\u0131\\u015f\\u0131k\", \"uzakl\\u0131kta\", \"romanya\", \"ad\\u0131yla\", \"bir\", \"olarak\", \"bulunan\", \"nin\", \"yakla\\u015f\\u0131k\", \"eden\", \"la\", \"ad\\u0131n\\u0131\", \"geldi\", \"oyuncusu\", \"dolay\\u0131\", \"g\\u00fcn\\u00fcm\\u00fczde\", \"d\\u00fcnyaya\", \"karar\", \"s\\u00fcper\", \"olmayan\", \"kent\", \"\\u00f6nceki\", \"aras\\u0131\", \"metre\", \"art\\u0131k\", \"1985\", \"\\u00e7al\\u0131\\u015fan\", \"tak\\u0131m\\u0131n\", \"1981\", \"konu\", \"galatasaray\", \"ifade\", \"do\\u011fumlu\", \"hareket\", \"40\", \"i\\u00e7eren\", \"be\\u015fikta\\u015f\", \"1975\", \"m\\u00fczi\\u011fi\", \"bir\", \"se\\u00e7ildi\", \"fransa\", \"amerika\", \"devlet\", \"birle\\u015fik\", \"new\", \"\\u00f6zellikle\", \"ba\\u015fkan\\u0131\", \"d\\u00f6neminde\", \"york\", \"ge\\u00e7en\", \"ona\", \"kurulmu\\u015ftur\", \"nden\", \"merkez\", \"y\\u0131ll\\u0131k\", \"devletleri\", \"krall\\u0131k\", \"japonya\", \"belediye\", \"de\\u011fil\", \"birisidir\", \"robert\", \"oyunu\", \"ad\\u0131na\", \"edilmi\\u015f\", \"adam\\u0131\", \"b\\u00f6l\\u00fcm\\u00fcnde\", \"krall\\u0131\\u011f\\u0131\", \"ki\\u015filik\", \"ada\", \"olarak\", \"sovyet\", \"t\\u00fcrkiye\", \"ayr\\u0131ca\", \"2008\", \"20\", \"yapt\\u0131\\u011f\\u0131\", \"19\", \"bunun\", \"edilen\", \"21\", \"g\\u00fcn\\u00fc\", \"piyasaya\", \"edilmi\\u015ftir\", \"ilan\", \"tak\\u0131m\\u0131n\\u0131n\", \"alan\\u0131\", \"k\\u0131br\\u0131s\", \"y\\u00f6netmenli\\u011fini\", \"records\", \"city\", \"k\\u00fclt\\u00fcr\", \"girmi\\u015ftir\", \"al\", \"y\\u00fczy\\u0131l\\u0131n\", \"buz\", \"denir\", \"ndaki\", \"kullan\\u0131lmaktad\\u0131r\", \"united\", \"manchester\", \"imza\", \"temmuz\", \"ard\\u0131ndan\", \"nin\", \"olarak\", \"2006\", \"2005\", \"futbolcu\", \"15\", \"yazar\", \"ait\", \"su\", \"beraber\", \"\\u00fcn\", \"hemen\", \"basketbol\", \"kul\\u00fcb\\u00fc\", \"nba\", \"beyaz\", \"sve\\u00e7\", \"ali\", \"yedi\", \"mustafa\", \"vard\\u0131\", \"1977\", \"itibariyle\", \"1969\", \"kendine\", \"muhammed\", \"zengin\", \"is\", \"arap\\u00e7a\", \"all\", \"star\", \"1963\", \"g\\u00f6revi\", \"bulunan\", \"da\", \"birlikte\", \"ye\", \"alman\", \"ocak\", \"uzun\", \"k\\u0131sa\", \"12\", \"temsil\", \"geni\\u015f\", \"arkada\\u015f\\u0131\", \"parti\", \"tan\", \"l\\u0131\", \"as\\u0131ll\\u0131\", \"para\", \"akt\\u00f6r\", \"halen\", \"e\\u015fi\", \"macaristan\", \"do\\u011fan\", \"yu\", \"von\", \"k\\u0131sm\\u0131\", \"kodu\", \"yaln\\u0131zca\", \"bulundu\", \"destek\", \"y\\u0131llar\", \"1920\", \"sosyalist\", \"de\", \"kas\\u0131m\", \"nun\", \"genel\", \"lk\", \"biridir\", \"1998\", \"bulundu\\u011fu\", \"\\u00fclke\", \"single\", \"y\\u0131llar\\u0131nda\", \"d\\u00fcnyan\\u0131n\", \"kanada\", \"kurulmu\\u015f\", \"avusturya\", \"roman\", \"bug\\u00fcn\", \"olmas\\u0131na\", \"belediyesi\", \"\\u015fark\\u0131n\\u0131n\", \"berlin\", \"ba\\u015fkan\", \"dur\", \"kurulu\", \"yay\\u0131n\", \"ya\\u015fad\\u0131\\u011f\\u0131\", \"\\u00e7ap\\u0131nda\", \"kar\\u015f\\u0131n\", \"tan\\u0131nan\", \"yay\\u0131mland\\u0131\", \"h\\u00fck\\u00fcmet\", \"b\\u00f6lgesine\", \"milyon\", \"2006\", \"nin\", \"nde\", \"orta\", \"fazla\", \"farkl\\u0131\", \"23\", \"sanat\", \"aday\", \"ge\\u00e7ti\", \"b\\u00f6l\\u00fcm\\u00fc\", \"michael\", \"etmek\", \"\\u015fehirdir\", \"\\u00f6d\\u00fclleri\", \"festivali\", \"sonucunda\", \"ederek\", \"normal\", \"\\u00f6d\\u00fcl\", \"y\\u00f6netmeni\", \"bilgisayar\", \"kullanarak\", \"akademi\", \"\\u015fehridir\", \"d\\u0131\\u015f\", \"yok\", \"kemal\", \"demir\", \"windows\", \"eyaletinin\", \"ba\\u015fbakan\", \"bir\", \"nin\"], \"Total\": [170986.0, 54793.0, 39207.0, 56463.0, 43846.0, 34235.0, 42140.0, 32995.0, 35334.0, 23744.0, 67489.0, 36427.0, 21682.0, 50081.0, 19403.0, 18292.0, 16536.0, 14895.0, 17009.0, 15881.0, 14391.0, 15004.0, 13756.0, 13064.0, 13645.0, 12697.0, 12875.0, 14968.0, 12667.0, 13753.0, 15004.832045306526, 12972.70599871714, 10229.485653198548, 6529.937674188665, 5107.201062289087, 5100.198827207377, 4931.880313024895, 4357.776102690324, 3968.29496019786, 3695.075251260438, 3489.000349671015, 3112.9410209194016, 3086.030430767423, 2793.9296230122172, 2385.691267418385, 2084.2456370552823, 1837.1603735589686, 1779.4014351452417, 1763.3319572399027, 1704.1833671919633, 1581.7088023653962, 1570.2783750364777, 1509.3190879383822, 1326.7490654581452, 1318.3809024890927, 1279.0555453570746, 1264.485119643491, 1251.4735785609514, 1220.678928854601, 1187.5304030895793, 43846.03450189743, 7144.031221003962, 6935.706056135619, 4009.434246729942, 2639.89274037169, 7791.636630383758, 5079.0025206475175, 5282.757202942151, 5220.943038098354, 17009.356421314384, 13753.395354700513, 11579.369912254473, 9678.229601422436, 8174.669404639866, 4032.5311564117947, 3828.7340401612605, 3588.847371538334, 3580.6181017569497, 2961.7219125958786, 2818.2822906273595, 2661.209364944987, 2655.5480345733445, 2564.1248377506745, 2397.603388414537, 2193.790651373838, 2023.1043961000012, 1965.6782938866638, 1883.8607685987022, 1872.3137685683719, 1838.3739379548351, 1655.435236645516, 1606.4354810155812, 1454.0832316201918, 1384.180466980281, 1338.1431467436955, 1316.7306063157378, 1275.8819607807905, 1237.7591212951868, 1125.1259942721572, 56463.44596055282, 5220.943038098354, 50081.84472377461, 2791.3552789077207, 32995.24744039874, 4981.8925521426045, 2651.7883802005467, 42140.862163423066, 2728.0840892014894, 67489.59013895788, 11411.16119365728, 10514.528866451928, 9960.769381564656, 9862.750723317224, 6799.242948736219, 5767.867500596139, 5330.599748311108, 4598.634431300197, 3878.3574610847945, 3738.4458758020046, 3685.1934549645443, 3682.6013206205903, 3526.8127223319966, 3478.6961975786508, 3113.9604197395947, 2862.611744998623, 2848.8539416454546, 2744.1487327398518, 2671.247815358662, 2520.0738109775816, 2200.31722886183, 2043.216468911899, 1962.4536526567133, 1914.4784011801942, 1725.6815288656064, 1667.3970290116329, 1657.3878140224024, 1603.1661152533013, 1573.209590562525, 1509.1953870613227, 8035.858781766912, 3409.5676494458844, 170986.5559028925, 56463.44596055282, 9166.323761706228, 5910.650218073049, 4928.880731951543, 4744.572648604179, 4227.713844121585, 4065.3823622312593, 3894.7476728834986, 3803.6713823486098, 3741.0178160600885, 3695.705784485761, 3644.263320169313, 3414.8903266571656, 3349.3472852042655, 3303.1510154315783, 3187.853527396562, 2925.390180304753, 2830.668563840747, 2773.1030739364724, 2773.1025963512766, 2613.683274908421, 2498.7808737863543, 2411.1845191392076, 2353.8210163483873, 2351.9274643170684, 2306.635034319276, 2110.3351477666265, 1907.0106289300895, 1931.057408101958, 1830.2230746985665, 1795.3819766451086, 6877.001201820895, 3930.4990140681434, 3900.502555561791, 50081.84472377461, 6699.378320283258, 6093.658037021654, 6046.944723892456, 4099.803628972212, 3082.1662867860177, 2848.7244587710798, 2456.184344928593, 2281.2321605397137, 2221.5705903682697, 2165.47247841858, 2157.465669092221, 2004.3731765536597, 1919.8083617875836, 1701.471533052389, 1691.3247688318875, 1647.4025601719804, 1634.716368478385, 1538.7655116714263, 1276.087614855102, 1208.6511376038156, 1203.348451849768, 1087.8416519691432, 1056.9446216977851, 1042.0465036647233, 1019.3631072500274, 976.7877699595371, 933.6913471147201, 923.6098548460067, 919.8303561269845, 918.9661414394349, 54793.74424102345, 170986.5559028925, 36427.032506436444, 34625.04033239265, 8035.858781766912, 7126.336812162653, 4066.21914826531, 8400.58537443314, 7758.713827166942, 6360.384677015684, 5844.782094441087, 5428.2050449977205, 5405.658890035088, 4941.769530124197, 4661.944817534431, 4484.133183700776, 3052.613346518648, 2807.0672718950755, 2791.6186553536554, 2634.8134567307875, 2616.512057898194, 2507.3201611717077, 2150.6396346912998, 2045.9411657918247, 2039.520483948002, 1982.4257503729877, 1827.9230739528332, 1734.377451560308, 1660.9320958527035, 1652.9916870509737, 1549.0359615798802, 1508.0060836753714, 1497.4928446813808, 1360.8440755487702, 1334.5178174760258, 1259.6110155920253, 1253.520653069451, 35334.37504994734, 4446.019954285079, 43846.03450189743, 2231.73996569235, 12994.652990540624, 9989.441920891824, 8073.301662174539, 7757.383250823763, 6465.410891769147, 6152.554553947787, 6142.202708282201, 5993.073989490826, 5750.393548187033, 5659.883614818395, 4310.174762986696, 3482.737874946564, 3397.99719545796, 3309.920031035929, 3254.5365793507385, 3012.6110777770514, 2875.753774106759, 2688.968189603248, 2480.0591918883256, 2338.555298119104, 2306.1297174112246, 2281.1663998962786, 2103.7824352329753, 2025.581221518404, 2024.5997758978594, 1911.2336918084548, 1895.6429959386592, 1844.7170972651538, 1827.741855735641, 1822.5380934019424, 8220.678317870981, 6209.349174717462, 2929.8441467246907, 67489.59013895788, 7091.198127578331, 23744.561562330775, 10670.748810527499, 8714.11661950857, 7402.986970298464, 5155.388161020749, 4275.164056180041, 3864.9198472855446, 3858.0540016921746, 3176.1633240132937, 2893.5101116704614, 2695.190235416037, 2453.0730472233377, 1938.8446205929404, 1925.4591805731338, 1718.1338302240376, 1549.6707403276266, 1483.9887590812746, 1443.8992373010194, 1428.6877139313378, 1391.8161917024472, 1389.8185999551379, 1365.406533391808, 1342.8939903893602, 1330.700501483381, 1319.1491647868177, 1313.3674685818087, 1262.0243367401176, 1248.2641773496737, 1221.320127946845, 1187.1116383251663, 6247.036343609765, 3203.171330125407, 3806.04664076846, 67489.59013895788, 2511.443568663458, 4038.7758997490178, 35334.37504994734, 4854.831720488976, 14391.814141766174, 12554.886728163186, 7914.237862945886, 7245.073893061146, 5297.904889862569, 5206.62014410544, 4395.696064740976, 3424.5646667298765, 3364.1629131708705, 3246.3954284829692, 3155.697585792943, 2513.2453821201657, 2460.3459798084873, 2382.7806383235156, 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\"kendini\", \"kendisi\", \"kendisine\", \"kendisini\", \"kent\", \"kenti\", \"kentinde\", \"ki\", \"kinci\", \"kitab\\u0131\", \"kitap\", \"ki\\u015fi\", \"ki\\u015fidir\", \"ki\\u015filik\", \"klasik\", \"km\", \"kodu\", \"komedi\", \"konu\", \"kore\", \"kral\", \"kraliyet\", \"krall\\u0131k\", \"krall\\u0131\\u011f\\u0131\", \"kral\\u0131\", \"kud\\u00fcs\", \"kullanarak\", \"kullan\\u0131lan\", \"kullan\\u0131lmaktad\\u0131r\", \"kullan\\u0131l\\u0131r\", \"kul\\u00fcb\\u00fc\", \"kul\\u00fcp\", \"kupas\\u0131\", \"kurgu\", \"kurucusu\", \"kurulan\", \"kuruldu\", \"kurulmu\\u015f\", \"kurulmu\\u015ftur\", \"kurulu\", \"kuzey\", \"kuzeyinde\", \"k\\u00f6t\\u00fc\", \"k\\u00f6y\", \"k\\u00f6yd\\u00fcr\", \"k\\u00fclt\\u00fcr\", \"k\\u00fc\\u00e7\\u00fck\", \"k\\u0131br\\u0131s\", \"k\\u0131sa\", \"k\\u0131sm\\u0131\", \"k\\u0131z\", \"k\\u0131z\\u0131\", \"la\", \"le\", \"li\", \"lideri\", \"lig\", \"ligi\", \"lisesi\", \"listesinde\", \"lk\", \"londra\", \"los\", \"love\", \"l\\u0131\", \"macaristan\", \"madalya\", \"manchester\", \"mart\", \"mart\", \"martin\", \"may\\u0131s\", \"ma\\u00e7\", \"ma\\u00e7ta\", \"ma\\u00e7\\u0131nda\", \"me\", \"mehmet\", \"meksika\", \"merkez\", \"merkezi\", \"merkezi\", \"merkezidir\", \"metal\", \"metre\", \"meydana\", \"mezun\", \"michael\", \"milli\", \"mill\\u00ee\", \"milyon\", \"milyon\", \"modern\", \"moskova\", \"mparatorlu\\u011fu\", \"muhammed\", \"mustafa\", \"m\\u00f6\", \"m\\u00fccadele\", \"m\\u00fczik\", \"m\\u00fczi\\u011fi\", \"m\\u0131s\\u0131r\", \"na\", \"nba\", \"nda\", \"ndaki\", \"ndan\", \"nde\", \"nden\", \"ne\", \"nedeniyle\", \"nedeniyle\", \"nedeniyle\", \"nedeniyle\", \"nedenle\", \"nehri\", \"new\", \"ngc\", \"ngiliz\", \"ngilizce\", \"ngiltere\", \"ni\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nin\", \"nisan\", \"normal\", \"norve\\u00e7\", \"nun\", \"n\\u00fcfus\", \"n\\u00fcfusu\", \"n\\u00fcn\", \"n\\u0131\", \"n\\u0131n\", \"n\\u0131n\", \"n\\u0131n\", \"ocak\", \"of\", \"of\", \"okulu\", \"olan\", \"olan\", \"olarak\", \"olarak\", \"olarak\", \"olarak\", \"olarak\", \"olarak\", \"olarak\", \"olarak\", \"olarak\", \"olarak\", \"olarak\", \"oldu\", \"olduk\\u00e7a\", \"oldu\\u011fu\", \"oldu\\u011funu\", \"olimpiyat\", \"olimpiyatlar\\u0131\", \"olma\", \"olmak\", \"olmas\\u0131\", \"olmas\\u0131na\", \"olmayan\", \"olmu\\u015f\", \"olmu\\u015ftur\", \"olsa\", \"olup\", \"olur\", \"olu\\u015fan\", \"olu\\u015fturan\", \"olu\\u015fur\", \"on\", \"ona\", \"onu\", \"onun\", \"ordu\", \"ordusu\", \"orijinal\", \"orta\", \"ortak\", \"ortaya\", \"ortaya\", \"osmanl\\u0131\", \"oynad\\u0131\", \"oynad\\u0131\\u011f\\u0131\", \"oynamaktad\\u0131r\", \"oynam\\u0131\\u015ft\\u0131r\", \"oyun\", \"oyuncu\", \"oyuncusu\", \"oyuncusudur\", \"oyunlar\\u0131\", \"oyunu\", \"oyunun\", \"o\\u011flu\", \"para\", \"paris\", \"parti\", \"partisi\", \"par\\u00e7as\\u0131\", \"paul\", \"pa\\u015fa\", \"pek\", \"person_name\", \"peter\", \"piyasaya\", \"polonya\", \"pop\", \"pop\\u00fcler\", \"profesyonel\", \"ran\", \"ra\\u011fmen\", \"records\", \"resmi\", \"ressam\", \"richard\", \"rlanda\", \"robert\", \"rock\", \"rol\", \"roma\", \"roman\", \"romanya\", \"roman\\u0131\", \"rus\", \"rusya\", \"sadece\", \"sahibi\", \"sahip\", \"sahiptir\", \"sahne\", \"san\", \"sanat\", \"sanat\\u00e7\\u0131\", \"sanat\\u00e7\\u0131s\\u0131\", \"sat\\u0131n\", \"sat\\u0131\\u015fa\", \"sava\\u015f\", \"sava\\u015f\\u0131\", \"savunma\", \"sayesinde\", \"say\\u0131\", \"say\\u0131da\", \"say\\u0131s\\u0131\", \"sa\\u011f\", \"sa\\u011flayan\", \"schleswig\", \"sene\", \"serbest\", \"ses\", \"sezon\", \"sezonu\", \"sezonunda\", \"se\\u00e7ildi\", \"se\\u00e7ildi\", \"sinema\", \"single\", \"sistemi\", \"siyasi\", \"slam\", \"sol\", \"son\", \"sona\", \"sonra\", \"sonradan\", \"sonraki\", \"sonras\\u0131nda\", \"sonucu\", \"sonucunda\", \"sonunda\", \"sosyal\", \"sosyalist\", \"sovyet\", \"sovyet\", \"spanya\", \"spor\", \"srail\", \"stadyumu\", \"stanbul\", \"star\", \"st\\u00fcdyo\", \"su\", \"sultan\", \"suriye\", \"sve\\u00e7\", \"svi\\u00e7re\", \"s\\u00f6z\", \"s\\u00f6zle\\u015fme\", \"s\\u00fcper\", \"s\\u00fcre\", \"s\\u00fcrekli\", \"s\\u00fcren\", \"s\\u0131k\", \"s\\u0131n\\u0131rlar\\u0131\", \"s\\u0131ra\", \"s\\u0131rada\", \"s\\u0131ras\\u0131nda\", \"ta\", \"tak\\u0131m\", \"tak\\u0131mlar\\u0131ndan\", \"tak\\u0131m\\u0131\", \"tak\\u0131m\\u0131\", \"tak\\u0131m\\u0131n\", \"tak\\u0131m\\u0131na\", \"tak\\u0131m\\u0131n\\u0131n\", \"talya\", \"talya\", \"talya\", \"talyan\", \"tam\", \"tamamen\", \"tan\", \"tanr\\u0131\", \"tan\\u0131nan\", \"taraf\\u0131ndan\", \"taraf\\u0131ndan\", \"taraf\\u0131ndan\", \"tarih\", \"tarihi\", \"tarihinde\", \"tarihleri\", \"ta\\u015f\\u0131yan\", \"te\", \"tek\", \"tek\", \"teknik\", \"tekrar\", \"tekrar\", \"televizyon\", \"temel\", \"temmuz\", \"temmuz\", \"temsil\", \"ten\", \"the\", \"thomas\", \"ticaret\", \"ticari\", \"tipi\", \"tiyatro\", \"tiyatro\", \"tiyatrosu\", \"to\", \"toplam\", \"transfer\", \"tur\", \"t\\u00fcr\", \"t\\u00fcrk\", \"t\\u00fcrkiye\", \"t\\u00fcrk\\u00e7e\", \"t\\u00fcr\\u00fc\", \"uefa\", \"ukrayna\", \"ulusal\", \"uluslararas\\u0131\", \"un\", \"united\", \"uygun\", \"uzak\", \"uzakl\\u0131kta\", \"uzay\", \"uzun\", \"van\", \"var\", \"vard\\u0131\", \"vard\\u0131r\", \"verdi\", \"verdi\\u011fi\", \"veren\", \"verildi\", \"verilen\", \"verilen\", \"verilmi\\u015ftir\", \"video\", \"video\", \"von\", \"william\", \"windows\", \"ya\", \"yahudi\", \"yakla\\u015f\\u0131k\", \"yakla\\u015f\\u0131k\", \"yak\\u0131n\", \"yaln\\u0131zca\", \"yana\", \"yan\\u0131\", \"yan\\u0131nda\", \"yapan\", \"yapmaktad\\u0131r\", \"yapm\\u0131\\u015f\", \"yapm\\u0131\\u015ft\\u0131r\", \"yapt\\u0131\", \"yapt\\u0131\\u011f\\u0131\", \"yap\\u0131lan\", \"yap\\u0131lan\", \"yap\\u0131lm\\u0131\\u015ft\\u0131r\", \"yap\\u0131m\\u0131\", \"yard\\u0131m\", \"yard\\u0131mc\\u0131\", \"yar\\u0131\", \"yar\\u0131\\u015fmas\\u0131\", \"yayg\\u0131n\", \"yay\\u0131mlanan\", \"yay\\u0131mland\\u0131\", \"yay\\u0131n\", \"yay\\u0131nlanan\", \"yay\\u0131nland\\u0131\", \"yaz\", \"yazar\", \"yazar\\u0131\", \"yazd\\u0131\", \"yazd\\u0131\\u011f\\u0131\", \"ya\\u015f\", \"ya\\u015fad\\u0131\\u011f\\u0131\", \"ya\\u015fayan\", \"ya\\u015f\\u0131nda\", \"ye\", \"yedi\", \"yeni\", \"yeniden\", \"yer\", \"yerel\", \"yeri\", \"yerine\", \"yerini\", \"yerle\\u015fim\", \"yi\", \"yine\", \"yok\", \"yol\", \"yolu\", \"york\", \"you\", \"yo\\u011fun\", \"yu\", \"yunan\", \"yunanistan\", \"y\\u00f6netim\", \"y\\u00f6netimi\", \"y\\u00f6netmen\", \"y\\u00f6netmeni\", \"y\\u00f6netmenli\\u011fini\", \"y\\u00f6netti\\u011fi\", \"y\\u00f6n\\u00fcnde\", \"y\\u00fcksek\", \"y\\u00fczy\\u0131l\", \"y\\u00fczy\\u0131lda\", \"y\\u00fczy\\u0131l\\u0131n\", \"y\\u00fcz\\u00f6l\\u00e7\\u00fcme\", \"y\\u00fcz\\u00fcnden\", \"y\\u0131\", \"y\\u0131l\", \"y\\u0131ld\\u0131z\", \"y\\u0131llar\", \"y\\u0131llarda\", \"y\\u0131llar\\u0131\", \"y\\u0131llar\\u0131nda\", \"y\\u0131ll\\u0131k\", \"y\\u0131l\\u0131\", \"y\\u0131l\\u0131n\", \"y\\u0131l\\u0131na\", \"y\\u0131l\\u0131nda\", \"y\\u0131l\\u0131nda\", \"y\\u0131l\\u0131ndan\", \"y\\u0131l\\u0131n\\u0131n\", \"zaman\", \"zamanda\", \"zengin\", \"zmir\", \"\\u00e7al\\u0131\\u015fan\", \"\\u00e7al\\u0131\\u015ft\\u0131\", \"\\u00e7ap\\u0131nda\", \"\\u00e7e\\u015fitli\", \"\\u00e7ift\", \"\\u00e7in\", \"\\u00e7izgi\", \"\\u00e7ocuk\", \"\\u00e7ocu\\u011fu\", \"\\u00e7o\\u011fu\", \"\\u00e7\\u0131kan\", \"\\u00e7\\u0131km\\u0131\\u015ft\\u0131r\", \"\\u00e7\\u0131kt\\u0131\", \"\\u00f6d\\u00fcl\", \"\\u00f6d\\u00fclleri\", \"\\u00f6d\\u00fcl\\u00fc\", \"\\u00f6d\\u00fcl\\u00fcn\\u00fc\", \"\\u00f6ld\\u00fc\", \"\\u00f6l\\u00fcm\", \"\\u00f6l\\u00fcm\\u00fcnden\", \"\\u00f6n\", \"\\u00f6nce\", \"\\u00f6nce\", \"\\u00f6nceki\", \"\\u00f6nemli\", \"\\u00f6zel\", \"\\u00f6zellikle\", \"\\u00f6zerk\", \"\\u00fclke\", \"\\u00fclkenin\", \"\\u00fcn\", \"\\u00fcniversite\", \"\\u00fcniversitesi\", \"\\u00fcnl\\u00fc\", \"\\u00fcst\", \"\\u00fcyesi\", \"\\u00fczere\", \"\\u00fczerinde\", \"\\u00fczerinden\", \"\\u00fczerine\", \"\\u00fczerine\", \"\\u00fc\\u00e7\", \"\\u00fc\\u00e7\\u00fcnc\\u00fc\", \"\\u0131n\", \"\\u0131n\", \"\\u0131n\", \"\\u0131n\", \"\\u0131\\u015f\\u0131k\", \"\\u015fair\", \"\\u015fampiyon\", \"\\u015fampiyonas\\u0131\", \"\\u015fampiyonu\", \"\\u015fark\\u0131\", \"\\u015fark\\u0131c\\u0131\", \"\\u015fark\\u0131c\\u0131\", \"\\u015fark\\u0131n\\u0131n\", \"\\u015fark\\u0131s\\u0131\", \"\\u015fehir\", \"\\u015fehirdir\", \"\\u015fehri\", \"\\u015fehridir\", \"\\u015fehrin\", \"\\u015fehrinde\", \"\\u015fehrinde\", \"\\u015fekilde\", \"\\u015feklinde\", \"\\u015firket\", \"\\u015firketi\", \"\\u015fubat\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [12, 4, 11, 15, 9, 5, 7, 13, 2, 28, 1, 20, 8, 26, 24, 16, 18, 10, 17, 6, 27, 14, 30, 22, 29, 19, 25, 3, 21, 23]};\n",
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      ],
      "text/plain": [
       "PreparedData(topic_coordinates=                x           y  topics  cluster      Freq\n",
       "topic                                                   \n",
       "11      68.485832  -44.890598       1        1  4.241549\n",
       "3     -108.907669  -41.941544       2        1  4.215781\n",
       "10     -76.268181   -6.012811       3        1  4.058919\n",
       "14    -120.533546    6.128340       4        1  3.928254\n",
       "8      -93.373375   46.048691       5        1  3.867865\n",
       "4       12.051845   -6.604597       6        1  3.757854\n",
       "6      -52.854237   27.594101       7        1  3.735176\n",
       "12     -57.870720 -120.988350       8        1  3.685046\n",
       "1       77.015190   53.169323       9        1  3.614834\n",
       "27     -58.513775  -42.636860      10        1  3.560858\n",
       "0       -9.240859 -122.289192      11        1  3.552107\n",
       "19     -66.940781   87.192451      12        1  3.469674\n",
       "7       38.338284   28.702713      13        1  3.462227\n",
       "25      -8.334374   25.031023      14        1  3.423457\n",
       "23     -30.100882   61.407063      15        1  3.373644\n",
       "15     -14.876504  -41.492172      16        1  3.250670\n",
       "17     111.778374  -34.738796      17        1  3.240593\n",
       "9       25.840981  -41.860916      18        1  3.202473\n",
       "16      55.322525   -6.638516      19        1  3.128877\n",
       "5       96.468285   11.541354      20        1  3.117736\n",
       "26     -87.247597  -81.993141      21        1  3.055216\n",
       "13      -9.679766  103.776978      22        1  2.951354\n",
       "29      39.560951 -125.245644      23        1  2.926291\n",
       "21      43.116650  -80.592110      24        1  2.906306\n",
       "28      13.957630   60.905731      25        1  2.903887\n",
       "18      88.884041  -89.513611      26        1  2.783273\n",
       "24     -38.837139  -78.677017      27        1  2.719903\n",
       "2      -32.076191   -7.829980      28        1  2.697177\n",
       "20       2.223633  -79.129692      29        1  2.585275\n",
       "22      46.673836   91.262482      30        1  2.583722, topic_info=     Category           Freq        Term          Total  loglift  logprob\n",
       "1462  Default  170986.000000         bir  170986.000000  30.0000  30.0000\n",
       "6269  Default   54793.000000        olan   54793.000000  29.0000  29.0000\n",
       "4232  Default   39207.000000          in   39207.000000  28.0000  28.0000\n",
       "2011  Default   56463.000000          da   56463.000000  27.0000  27.0000\n",
       "9276  Default   43846.000000     yılında   43846.000000  26.0000  26.0000\n",
       "...       ...            ...         ...            ...      ...      ...\n",
       "8683  Topic30    1148.025736     windows    1148.986680   3.6551  -4.9906\n",
       "2940  Topic30    1043.704133  eyaletinin    1044.665077   3.6550  -5.0859\n",
       "1234  Topic30    1036.042672    başbakan    1037.003616   3.6550  -5.0932\n",
       "1462  Topic30    7946.257055         bir  170986.555903   0.5871  -3.0560\n",
       "6146  Topic30    1066.158347         nin   34625.040332   0.1754  -5.0646\n",
       "\n",
       "[1070 rows x 6 columns], token_table=      Topic      Freq      Term\n",
       "term                           \n",
       "9         7  0.999782        10\n",
       "10       13  0.999822       100\n",
       "18        4  0.363953        11\n",
       "18       13  0.517858        11\n",
       "18       17  0.118138        11\n",
       "...     ...       ...       ...\n",
       "9937     11  0.999841   şekilde\n",
       "9940     11  0.999410  şeklinde\n",
       "9969      6  0.999585    şirket\n",
       "9970     18  0.999310   şirketi\n",
       "9988     21  0.999754     şubat\n",
       "\n",
       "[1090 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[12, 4, 11, 15, 9, 5, 7, 13, 2, 28, 1, 20, 8, 26, 24, 16, 18, 10, 17, 6, 27, 14, 30, 22, 29, 19, 25, 3, 21, 23])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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